Overview

Dataset statistics

Number of variables20
Number of observations22586
Missing cells7968
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 MiB
Average record size in memory427.6 B

Variable types

Categorical7
DateTime3
Numeric8
Text2

Alerts

Tipo de registro del contacto has constant value ""Constant
RFM Segmento Actual has constant value ""Constant
Donante Activo has constant value ""Constant
Cantidad Cuotas Pagadas Global is highly overall correlated with Edad_donacionHigh correlation
Edad_donacion is highly overall correlated with Cantidad Cuotas Pagadas GlobalHigh correlation
Otra Clasificación RFM Actual is highly imbalanced (62.8%)Imbalance
Género has 388 (1.7%) missing valuesMissing
Estado Civil has 2399 (10.6%) missing valuesMissing
Ocupación has 4585 (20.3%) missing valuesMissing
Monto Actual is highly skewed (γ1 = 58.50801011)Skewed
Cantidad de Hijos has 7125 (31.5%) zerosZeros
Lapsed Probability has 19654 (87.0%) zerosZeros
Cantidad Cuotas No Pagadas Global has 8950 (39.6%) zerosZeros
Edad_donacion has 439 (1.9%) zerosZeros

Reproduction

Analysis started2023-08-31 02:45:41.618310
Analysis finished2023-08-31 02:45:46.617283
Duration5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Tipo de registro del contacto
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.7 KiB
Donante
22586 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters158102
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDonante
2nd rowDonante
3rd rowDonante
4th rowDonante
5th rowDonante

Common Values

ValueCountFrequency (%)
Donante 22586
100.0%

Length

2023-08-30T21:45:46.646718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:46.699693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
donante 22586
100.0%

Most occurring characters

ValueCountFrequency (%)
n 45172
28.6%
D 22586
14.3%
o 22586
14.3%
a 22586
14.3%
t 22586
14.3%
e 22586
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135516
85.7%
Uppercase Letter 22586
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 45172
33.3%
o 22586
16.7%
a 22586
16.7%
t 22586
16.7%
e 22586
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 22586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 158102
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 45172
28.6%
D 22586
14.3%
o 22586
14.3%
a 22586
14.3%
t 22586
14.3%
e 22586
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 45172
28.6%
D 22586
14.3%
o 22586
14.3%
a 22586
14.3%
t 22586
14.3%
e 22586
14.3%
Distinct11661
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size352.9 KiB
Minimum1932-10-11 00:00:00
Maximum2004-12-21 00:00:00
2023-08-30T21:45:46.747688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:46.814650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct365
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size352.9 KiB
Minimum2023-01-01 00:00:00
Maximum2023-12-31 00:00:00
2023-08-30T21:45:46.877211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:46.939948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Edad
Real number (ℝ)

Distinct73
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.436554
Minimum18
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:47.010114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q134
median41
Q348
95-th percentile67
Maximum90
Range72
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.999841
Coefficient of variation (CV)0.28277133
Kurtosis1.1365991
Mean42.436554
Median Absolute Deviation (MAD)7
Skewness1.0092894
Sum958472
Variance143.99617
MonotonicityNot monotonic
2023-08-30T21:45:47.076176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 1024
 
4.5%
41 965
 
4.3%
40 935
 
4.1%
37 923
 
4.1%
39 922
 
4.1%
43 907
 
4.0%
38 907
 
4.0%
36 864
 
3.8%
34 820
 
3.6%
33 819
 
3.6%
Other values (63) 13500
59.8%
ValueCountFrequency (%)
18 44
 
0.2%
19 69
 
0.3%
20 32
 
0.1%
21 44
 
0.2%
22 63
 
0.3%
23 104
 
0.5%
24 216
1.0%
25 196
0.9%
26 242
1.1%
27 383
1.7%
ValueCountFrequency (%)
90 5
 
< 0.1%
89 7
 
< 0.1%
88 9
 
< 0.1%
87 16
0.1%
86 9
 
< 0.1%
85 32
0.1%
84 27
0.1%
83 23
0.1%
82 31
0.1%
81 33
0.1%
Distinct2772
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size352.9 KiB
Minimum2004-04-22 00:00:00
Maximum2023-01-30 00:00:00
2023-08-30T21:45:47.143582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:47.205046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Monto Actual
Real number (ℝ)

SKEWED 

Distinct91
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36624.446
Minimum1667
Maximum4500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:47.273230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1667
5-th percentile20000
Q130000
median30000
Q350000
95-th percentile60000
Maximum4500000
Range4498333
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation43206.602
Coefficient of variation (CV)1.1797203
Kurtosis5329.102
Mean36624.446
Median Absolute Deviation (MAD)10000
Skewness58.50801
Sum8.2719974 × 108
Variance1.8668104 × 109
MonotonicityNot monotonic
2023-08-30T21:45:47.335782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 9195
40.7%
50000 5099
22.6%
20000 4060
18.0%
25000 824
 
3.6%
40000 760
 
3.4%
35000 642
 
2.8%
60000 516
 
2.3%
15000 285
 
1.3%
100000 277
 
1.2%
10000 238
 
1.1%
Other values (81) 690
 
3.1%
ValueCountFrequency (%)
1667 1
 
< 0.1%
2000 1
 
< 0.1%
4000 1
 
< 0.1%
5000 8
 
< 0.1%
6667 4
 
< 0.1%
7000 1
 
< 0.1%
7500 2
 
< 0.1%
8333 2
 
< 0.1%
8334 1
 
< 0.1%
10000 238
1.1%
ValueCountFrequency (%)
4500000 1
 
< 0.1%
1650000 1
 
< 0.1%
1500000 2
 
< 0.1%
1300000 1
 
< 0.1%
1200000 1
 
< 0.1%
1000000 2
 
< 0.1%
650000 1
 
< 0.1%
520000 1
 
< 0.1%
500000 4
< 0.1%
400000 5
< 0.1%

Género
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing388
Missing (%)1.7%
Memory size198.8 KiB
Femenino
14775 
Masculino
7416 
Otro
 
7

Length

Max length9
Median length8
Mean length8.3328228
Min length4

Characters and Unicode

Total characters184972
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowFemenino
3rd rowMasculino
4th rowFemenino
5th rowFemenino

Common Values

ValueCountFrequency (%)
Femenino 14775
65.4%
Masculino 7416
32.8%
Otro 7
 
< 0.1%
(Missing) 388
 
1.7%

Length

2023-08-30T21:45:47.392945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:47.449922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
femenino 14775
66.6%
masculino 7416
33.4%
otro 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 36966
20.0%
e 29550
16.0%
o 22198
12.0%
i 22191
12.0%
F 14775
 
8.0%
m 14775
 
8.0%
M 7416
 
4.0%
a 7416
 
4.0%
s 7416
 
4.0%
c 7416
 
4.0%
Other values (5) 14853
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162774
88.0%
Uppercase Letter 22198
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 36966
22.7%
e 29550
18.2%
o 22198
13.6%
i 22191
13.6%
m 14775
 
9.1%
a 7416
 
4.6%
s 7416
 
4.6%
c 7416
 
4.6%
u 7416
 
4.6%
l 7416
 
4.6%
Other values (2) 14
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 14775
66.6%
M 7416
33.4%
O 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 184972
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 36966
20.0%
e 29550
16.0%
o 22198
12.0%
i 22191
12.0%
F 14775
 
8.0%
m 14775
 
8.0%
M 7416
 
4.0%
a 7416
 
4.0%
s 7416
 
4.0%
c 7416
 
4.0%
Other values (5) 14853
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 36966
20.0%
e 29550
16.0%
o 22198
12.0%
i 22191
12.0%
F 14775
 
8.0%
m 14775
 
8.0%
M 7416
 
4.0%
a 7416
 
4.0%
s 7416
 
4.0%
c 7416
 
4.0%
Other values (5) 14853
8.0%

Estado Civil
Categorical

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing2399
Missing (%)10.6%
Memory size199.2 KiB
Casado
9367 
Soltero
6389 
Concubinato
3305 
Divorciado
 
454
Separado
 
421
Other values (2)
 
251

Length

Max length11
Median length10
Mean length7.2552138
Min length5

Characters and Unicode

Total characters146461
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasado
2nd rowCasado
3rd rowCasado
4th rowSoltero
5th rowCasado

Common Values

ValueCountFrequency (%)
Casado 9367
41.5%
Soltero 6389
28.3%
Concubinato 3305
 
14.6%
Divorciado 454
 
2.0%
Separado 421
 
1.9%
Viudo 248
 
1.1%
UNION LIBRE 3
 
< 0.1%
(Missing) 2399
 
10.6%

Length

2023-08-30T21:45:47.502346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:47.568080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
casado 9367
46.4%
soltero 6389
31.6%
concubinato 3305
 
16.4%
divorciado 454
 
2.2%
separado 421
 
2.1%
viudo 248
 
1.2%
union 3
 
< 0.1%
libre 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 30332
20.7%
a 23335
15.9%
C 12672
8.7%
d 10490
 
7.2%
t 9694
 
6.6%
s 9367
 
6.4%
r 7264
 
5.0%
S 6810
 
4.6%
e 6810
 
4.6%
n 6610
 
4.5%
Other values (18) 23077
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126244
86.2%
Uppercase Letter 20214
 
13.8%
Space Separator 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 30332
24.0%
a 23335
18.5%
d 10490
 
8.3%
t 9694
 
7.7%
s 9367
 
7.4%
r 7264
 
5.8%
e 6810
 
5.4%
n 6610
 
5.2%
l 6389
 
5.1%
i 4461
 
3.5%
Other values (5) 11492
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 12672
62.7%
S 6810
33.7%
D 454
 
2.2%
V 248
 
1.2%
N 6
 
< 0.1%
I 6
 
< 0.1%
U 3
 
< 0.1%
O 3
 
< 0.1%
L 3
 
< 0.1%
B 3
 
< 0.1%
Other values (2) 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 146458
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 30332
20.7%
a 23335
15.9%
C 12672
8.7%
d 10490
 
7.2%
t 9694
 
6.6%
s 9367
 
6.4%
r 7264
 
5.0%
S 6810
 
4.6%
e 6810
 
4.6%
n 6610
 
4.5%
Other values (17) 23074
15.8%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 30332
20.7%
a 23335
15.9%
C 12672
8.7%
d 10490
 
7.2%
t 9694
 
6.6%
s 9367
 
6.4%
r 7264
 
5.0%
S 6810
 
4.6%
e 6810
 
4.6%
n 6610
 
4.5%
Other values (18) 23077
15.8%

Cantidad de Hijos
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1463739
Minimum0
Maximum10
Zeros7125
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:47.624348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.033426
Coefficient of variation (CV)0.90147383
Kurtosis1.2981693
Mean1.1463739
Median Absolute Deviation (MAD)1
Skewness0.86516664
Sum25892
Variance1.0679694
MonotonicityNot monotonic
2023-08-30T21:45:47.673003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7625
33.8%
0 7125
31.5%
2 5912
26.2%
3 1442
 
6.4%
4 337
 
1.5%
5 116
 
0.5%
6 20
 
0.1%
7 6
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 7125
31.5%
1 7625
33.8%
2 5912
26.2%
3 1442
 
6.4%
4 337
 
1.5%
5 116
 
0.5%
6 20
 
0.1%
7 6
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 6
 
< 0.1%
6 20
 
0.1%
5 116
 
0.5%
4 337
 
1.5%
3 1442
 
6.4%
2 5912
26.2%
1 7625
33.8%

Tiene hijos
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.7 KiB
Si
15461 
No
7125 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters45172
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowSi
3rd rowSi
4th rowNo
5th rowSi

Common Values

ValueCountFrequency (%)
Si 15461
68.5%
No 7125
31.5%

Length

2023-08-30T21:45:47.722816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:47.771713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 15461
68.5%
no 7125
31.5%

Most occurring characters

ValueCountFrequency (%)
S 15461
34.2%
i 15461
34.2%
N 7125
15.8%
o 7125
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22586
50.0%
Lowercase Letter 22586
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 15461
68.5%
N 7125
31.5%
Lowercase Letter
ValueCountFrequency (%)
i 15461
68.5%
o 7125
31.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 45172
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 15461
34.2%
i 15461
34.2%
N 7125
15.8%
o 7125
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 15461
34.2%
i 15461
34.2%
N 7125
15.8%
o 7125
15.8%

Ocupación
Text

MISSING 

Distinct145
Distinct (%)0.8%
Missing4585
Missing (%)20.3%
Memory size1.5 MiB
2023-08-30T21:45:47.897910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length50
Median length39
Mean length10.300317
Min length4

Characters and Unicode

Total characters185416
Distinct characters72
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowPolitico
2nd rowJubilado
3rd rowIngeniero
4th rowEmpleado
5th rowAbogado
ValueCountFrequency (%)
empleado 2867
 
13.4%
ingeniero 2838
 
13.2%
administrador 1458
 
6.8%
profesor 936
 
4.4%
abogado 927
 
4.3%
de 878
 
4.1%
contador 784
 
3.7%
otra 715
 
3.3%
independiente 656
 
3.1%
trabajador 656
 
3.1%
Other values (164) 8740
40.7%
2023-08-30T21:45:48.197777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 20574
 
11.1%
e 17979
 
9.7%
a 15902
 
8.6%
r 14931
 
8.1%
i 14085
 
7.6%
n 13297
 
7.2%
d 13040
 
7.0%
t 7994
 
4.3%
s 7305
 
3.9%
m 6642
 
3.6%
Other values (62) 53667
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159922
86.3%
Uppercase Letter 21463
 
11.6%
Space Separator 3454
 
1.9%
Other Punctuation 532
 
0.3%
Control 43
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 20574
12.9%
e 17979
11.2%
a 15902
9.9%
r 14931
9.3%
i 14085
8.8%
n 13297
8.3%
d 13040
8.2%
t 7994
 
5.0%
s 7305
 
4.6%
m 6642
 
4.2%
Other values (21) 28173
17.6%
Uppercase Letter
ValueCountFrequency (%)
E 4088
19.0%
A 4039
18.8%
I 3704
17.3%
C 2278
10.6%
P 2067
9.6%
O 1042
 
4.9%
T 1025
 
4.8%
M 1002
 
4.7%
S 500
 
2.3%
D 454
 
2.1%
Other values (19) 1264
 
5.9%
Control
ValueCountFrequency (%)
“ 28
65.1%
‘ 9
 
20.9%
 3
 
7.0%
‰ 1
 
2.3%
š 1
 
2.3%
 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
/ 510
95.9%
, 15
 
2.8%
? 7
 
1.3%
Space Separator
ValueCountFrequency (%)
3454
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 181385
97.8%
Common 4031
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 20574
11.3%
e 17979
 
9.9%
a 15902
 
8.8%
r 14931
 
8.2%
i 14085
 
7.8%
n 13297
 
7.3%
d 13040
 
7.2%
t 7994
 
4.4%
s 7305
 
4.0%
m 6642
 
3.7%
Other values (50) 49636
27.4%
Common
ValueCountFrequency (%)
3454
85.7%
/ 510
 
12.7%
“ 28
 
0.7%
, 15
 
0.4%
‘ 9
 
0.2%
? 7
 
0.2%
 3
 
0.1%
‰ 1
 
< 0.1%
š 1
 
< 0.1%
( 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183004
98.7%
None 2412
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 20574
11.2%
e 17979
 
9.8%
a 15902
 
8.7%
r 14931
 
8.2%
i 14085
 
7.7%
n 13297
 
7.3%
d 13040
 
7.1%
t 7994
 
4.4%
s 7305
 
4.0%
m 6642
 
3.6%
Other values (41) 51255
28.0%
None
ValueCountFrequency (%)
ó 1167
48.4%
é 613
25.4%
ñ 328
 
13.6%
í 125
 
5.2%
á 45
 
1.9%
ú 34
 
1.4%
Ó 32
 
1.3%
“ 28
 
1.2%
Ñ 12
 
0.5%
‘ 9
 
0.4%
Other values (11) 19
 
0.8%

Churn Probability
Real number (ℝ)

Distinct585
Distinct (%)2.6%
Missing159
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.36757435
Minimum0.0101
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:48.282187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0101
5-th percentile0.0119
Q10.0372
median0.34
Q30.57
95-th percentile0.89
Maximum1
Range0.9899
Interquartile range (IQR)0.5328

Descriptive statistics

Standard deviation0.28711504
Coefficient of variation (CV)0.78110738
Kurtosis-0.82946895
Mean0.36757435
Median Absolute Deviation (MAD)0.26
Skewness0.42480846
Sum8243.5899
Variance0.082435044
MonotonicityNot monotonic
2023-08-30T21:45:48.348858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 518
 
2.3%
0.32 513
 
2.3%
0.34 511
 
2.3%
0.36 494
 
2.2%
0.37 488
 
2.2%
0.35 485
 
2.1%
0.31 476
 
2.1%
0.3 457
 
2.0%
0.38 415
 
1.8%
0.29 392
 
1.7%
Other values (575) 17678
78.3%
ValueCountFrequency (%)
0.0101 75
0.3%
0.0102 77
0.3%
0.0103 85
0.4%
0.0104 89
0.4%
0.0105 71
0.3%
0.0106 63
0.3%
0.0107 65
0.3%
0.0108 53
0.2%
0.0109 46
0.2%
0.011 58
0.3%
ValueCountFrequency (%)
1 77
0.3%
0.99 84
0.4%
0.98 99
0.4%
0.97 101
0.4%
0.96 100
0.4%
0.95 101
0.4%
0.94 101
0.4%
0.93 111
0.5%
0.92 103
0.5%
0.91 119
0.5%

Lapsed Probability
Real number (ℝ)

ZEROS 

Distinct2093
Distinct (%)9.3%
Missing159
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.063024132
Minimum0
Maximum0.8815
Zeros19654
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:48.415622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.55507
Maximum0.8815
Range0.8815
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17401347
Coefficient of variation (CV)2.7610609
Kurtosis5.1199635
Mean0.063024132
Median Absolute Deviation (MAD)0
Skewness2.5757195
Sum1413.4422
Variance0.030280686
MonotonicityNot monotonic
2023-08-30T21:45:48.477875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19654
87.0%
0.5402 6
 
< 0.1%
0.5715 5
 
< 0.1%
0.5973 5
 
< 0.1%
0.5987 5
 
< 0.1%
0.5645 4
 
< 0.1%
0.558 4
 
< 0.1%
0.6087 4
 
< 0.1%
0.5824 4
 
< 0.1%
0.5381 4
 
< 0.1%
Other values (2083) 2732
 
12.1%
(Missing) 159
 
0.7%
ValueCountFrequency (%)
0 19654
87.0%
0.089 1
 
< 0.1%
0.09 1
 
< 0.1%
0.0907 1
 
< 0.1%
0.0919 1
 
< 0.1%
0.0993 1
 
< 0.1%
0.1039 1
 
< 0.1%
0.1057 1
 
< 0.1%
0.1122 1
 
< 0.1%
0.1192 1
 
< 0.1%
ValueCountFrequency (%)
0.8815 1
< 0.1%
0.8793 1
< 0.1%
0.8762 1
< 0.1%
0.8691 1
< 0.1%
0.8685 1
< 0.1%
0.8661 1
< 0.1%
0.8651 1
< 0.1%
0.8616 1
< 0.1%
0.8591 1
< 0.1%
0.8561 1
< 0.1%

RFM Segmento Actual
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing78
Missing (%)0.3%
Memory size2.7 MiB
Otra Clasificación
22508 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters405144
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOtra Clasificación
2nd rowOtra Clasificación
3rd rowOtra Clasificación
4th rowOtra Clasificación
5th rowOtra Clasificación

Common Values

ValueCountFrequency (%)
Otra Clasificación 22508
99.7%
(Missing) 78
 
0.3%

Length

2023-08-30T21:45:48.533146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:48.580970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
otra 22508
50.0%
clasificación 22508
50.0%

Most occurring characters

ValueCountFrequency (%)
a 67524
16.7%
i 67524
16.7%
c 45016
11.1%
O 22508
 
5.6%
t 22508
 
5.6%
r 22508
 
5.6%
22508
 
5.6%
C 22508
 
5.6%
l 22508
 
5.6%
s 22508
 
5.6%
Other values (3) 67524
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 337620
83.3%
Uppercase Letter 45016
 
11.1%
Space Separator 22508
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 67524
20.0%
i 67524
20.0%
c 45016
13.3%
t 22508
 
6.7%
r 22508
 
6.7%
l 22508
 
6.7%
s 22508
 
6.7%
f 22508
 
6.7%
ó 22508
 
6.7%
n 22508
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
O 22508
50.0%
C 22508
50.0%
Space Separator
ValueCountFrequency (%)
22508
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 382636
94.4%
Common 22508
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 67524
17.6%
i 67524
17.6%
c 45016
11.8%
O 22508
 
5.9%
t 22508
 
5.9%
r 22508
 
5.9%
C 22508
 
5.9%
l 22508
 
5.9%
s 22508
 
5.9%
f 22508
 
5.9%
Other values (2) 45016
11.8%
Common
ValueCountFrequency (%)
22508
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382636
94.4%
None 22508
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 67524
17.6%
i 67524
17.6%
c 45016
11.8%
O 22508
 
5.9%
t 22508
 
5.9%
r 22508
 
5.9%
22508
 
5.9%
C 22508
 
5.9%
l 22508
 
5.9%
s 22508
 
5.9%
Other values (2) 45016
11.8%
None
ValueCountFrequency (%)
ó 22508
100.0%

Otra Clasificación RFM Actual
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing78
Missing (%)0.3%
Memory size1.6 MiB
Constantes
19095 
Ideales
2699 
Distantes
 
624
Extraviados
 
90

Length

Max length11
Median length10
Mean length9.6165363
Min length7

Characters and Unicode

Total characters216449
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConstantes
2nd rowConstantes
3rd rowConstantes
4th rowConstantes
5th rowConstantes

Common Values

ValueCountFrequency (%)
Constantes 19095
84.5%
Ideales 2699
 
11.9%
Distantes 624
 
2.8%
Extraviados 90
 
0.4%
(Missing) 78
 
0.3%

Length

2023-08-30T21:45:48.626278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:48.684861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
constantes 19095
84.8%
ideales 2699
 
12.0%
distantes 624
 
2.8%
extraviados 90
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s 42227
19.5%
t 39528
18.3%
n 38814
17.9%
e 25117
11.6%
a 22598
10.4%
o 19185
8.9%
C 19095
8.8%
d 2789
 
1.3%
I 2699
 
1.2%
l 2699
 
1.2%
Other values (6) 1698
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 193941
89.6%
Uppercase Letter 22508
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42227
21.8%
t 39528
20.4%
n 38814
20.0%
e 25117
13.0%
a 22598
11.7%
o 19185
9.9%
d 2789
 
1.4%
l 2699
 
1.4%
i 714
 
0.4%
x 90
 
< 0.1%
Other values (2) 180
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 19095
84.8%
I 2699
 
12.0%
D 624
 
2.8%
E 90
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 216449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42227
19.5%
t 39528
18.3%
n 38814
17.9%
e 25117
11.6%
a 22598
10.4%
o 19185
8.9%
C 19095
8.8%
d 2789
 
1.3%
I 2699
 
1.2%
l 2699
 
1.2%
Other values (6) 1698
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 42227
19.5%
t 39528
18.3%
n 38814
17.9%
e 25117
11.6%
a 22598
10.4%
o 19185
8.9%
C 19095
8.8%
d 2789
 
1.3%
I 2699
 
1.2%
l 2699
 
1.2%
Other values (6) 1698
 
0.8%

Cantidad Cuotas Pagadas Global
Real number (ℝ)

HIGH CORRELATION 

Distinct260
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.877978
Minimum0
Maximum358
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:48.740069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q114
median31
Q368
95-th percentile122
Maximum358
Range358
Interquartile range (IQR)54

Descriptive statistics

Standard deviation43.241148
Coefficient of variation (CV)0.94252515
Kurtosis4.7904442
Mean45.877978
Median Absolute Deviation (MAD)21
Skewness1.8925268
Sum1036200
Variance1869.7968
MonotonicityNot monotonic
2023-08-30T21:45:48.803397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 626
 
2.8%
12 609
 
2.7%
10 587
 
2.6%
8 585
 
2.6%
11 563
 
2.5%
14 526
 
2.3%
6 516
 
2.3%
13 509
 
2.3%
15 499
 
2.2%
16 494
 
2.2%
Other values (250) 17072
75.6%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 104
 
0.5%
2 130
 
0.6%
3 196
 
0.9%
4 231
 
1.0%
5 298
1.3%
6 516
2.3%
7 492
2.2%
8 585
2.6%
9 626
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
340 1
< 0.1%
336 1
< 0.1%
320 1
< 0.1%
318 1
< 0.1%
317 1
< 0.1%
316 2
< 0.1%
312 2
< 0.1%
310 1
< 0.1%
308 2
< 0.1%

Cantidad Cuotas No Pagadas Global
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5903657
Minimum0
Maximum118
Zeros8950
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size352.9 KiB
2023-08-30T21:45:48.869090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile30
Maximum118
Range118
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.418363
Coefficient of variation (CV)1.7325841
Kurtosis11.560495
Mean6.5903657
Median Absolute Deviation (MAD)2
Skewness2.9761687
Sum148850
Variance130.37902
MonotonicityNot monotonic
2023-08-30T21:45:48.935220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8950
39.6%
1 2193
 
9.7%
2 1304
 
5.8%
4 1148
 
5.1%
3 1064
 
4.7%
5 848
 
3.8%
6 650
 
2.9%
8 490
 
2.2%
7 486
 
2.2%
12 404
 
1.8%
Other values (89) 5049
22.4%
ValueCountFrequency (%)
0 8950
39.6%
1 2193
 
9.7%
2 1304
 
5.8%
3 1064
 
4.7%
4 1148
 
5.1%
5 848
 
3.8%
6 650
 
2.9%
7 486
 
2.2%
8 490
 
2.2%
9 390
 
1.7%
ValueCountFrequency (%)
118 1
< 0.1%
114 1
< 0.1%
107 1
< 0.1%
105 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
Distinct98
Distinct (%)0.4%
Missing122
Missing (%)0.5%
Memory size1.8 MiB
2023-08-30T21:45:49.048102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length46
Median length44
Mean length15.257968
Min length6

Characters and Unicode

Total characters342755
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st rowCOF2FR.F. 2021
2nd rowCOWCB R.F.2022
3rd rowSPIF F2F 2016
4th rowCOF2FR.F.2022
5th rowCOWCB R.F.2022
ValueCountFrequency (%)
f2f 7917
 
14.2%
cof2fr.f.2022 3926
 
7.0%
cowcb 3592
 
6.4%
spif 2864
 
5.1%
2019 2751
 
4.9%
2018 2588
 
4.6%
para 2486
 
4.5%
2021 2451
 
4.4%
cof2fr.f 2430
 
4.4%
r.f.2022 2070
 
3.7%
Other values (120) 22717
40.7%
2023-08-30T21:45:49.251634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 54369
15.9%
F 45077
13.2%
33537
 
9.8%
0 22936
 
6.7%
. 21692
 
6.3%
R 18666
 
5.4%
C 17803
 
5.2%
I 15602
 
4.6%
O 13862
 
4.0%
1 13748
 
4.0%
Other values (56) 85463
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 170875
49.9%
Decimal Number 102902
30.0%
Space Separator 33537
 
9.8%
Other Punctuation 22174
 
6.5%
Lowercase Letter 9838
 
2.9%
Dash Punctuation 2346
 
0.7%
Connector Punctuation 519
 
0.2%
Close Punctuation 282
 
0.1%
Open Punctuation 282
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1999
20.3%
s 1075
10.9%
e 1075
10.9%
a 757
 
7.7%
d 637
 
6.5%
c 615
 
6.3%
b 587
 
6.0%
u 482
 
4.9%
k 382
 
3.9%
i 369
 
3.8%
Other values (15) 1860
18.9%
Uppercase Letter
ValueCountFrequency (%)
F 45077
26.4%
R 18666
10.9%
C 17803
 
10.4%
I 15602
 
9.1%
O 13862
 
8.1%
S 8842
 
5.2%
A 8200
 
4.8%
P 6766
 
4.0%
E 5987
 
3.5%
N 4921
 
2.9%
Other values (14) 25149
14.7%
Decimal Number
ValueCountFrequency (%)
2 54369
52.8%
0 22936
22.3%
1 13748
 
13.4%
9 2759
 
2.7%
8 2588
 
2.5%
6 1975
 
1.9%
7 1333
 
1.3%
3 1172
 
1.1%
4 1133
 
1.1%
5 889
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 21692
97.8%
# 482
 
2.2%
Space Separator
ValueCountFrequency (%)
33537
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2346
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 519
100.0%
Close Punctuation
ValueCountFrequency (%)
) 282
100.0%
Open Punctuation
ValueCountFrequency (%)
( 282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180713
52.7%
Common 162042
47.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 45077
24.9%
R 18666
10.3%
C 17803
 
9.9%
I 15602
 
8.6%
O 13862
 
7.7%
S 8842
 
4.9%
A 8200
 
4.5%
P 6766
 
3.7%
E 5987
 
3.3%
N 4921
 
2.7%
Other values (39) 34987
19.4%
Common
ValueCountFrequency (%)
2 54369
33.6%
33537
20.7%
0 22936
14.2%
. 21692
 
13.4%
1 13748
 
8.5%
9 2759
 
1.7%
8 2588
 
1.6%
- 2346
 
1.4%
6 1975
 
1.2%
7 1333
 
0.8%
Other values (7) 4759
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341643
99.7%
None 1112
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 54369
15.9%
F 45077
13.2%
33537
 
9.8%
0 22936
 
6.7%
. 21692
 
6.3%
R 18666
 
5.5%
C 17803
 
5.2%
I 15602
 
4.6%
O 13862
 
4.1%
1 13748
 
4.0%
Other values (53) 84351
24.7%
None
ValueCountFrequency (%)
Ó 1052
94.6%
ó 59
 
5.3%
á 1
 
0.1%

Donante Activo
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
22586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22586
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22586
100.0%

Length

2023-08-30T21:45:49.324564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-30T21:45:49.371527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 22586
100.0%

Most occurring characters

ValueCountFrequency (%)
1 22586
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22586
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22586
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22586
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22586
100.0%

Edad_donacion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2821659
Minimum0
Maximum19
Zeros439
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size264.7 KiB
2023-08-30T21:45:49.409846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4694451
Coefficient of variation (CV)0.810208
Kurtosis0.5393155
Mean4.2821659
Median Absolute Deviation (MAD)2
Skewness1.0368323
Sum96717
Variance12.037049
MonotonicityNot monotonic
2023-08-30T21:45:49.460883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 5909
26.2%
2 3895
17.2%
4 2178
 
9.6%
7 1918
 
8.5%
8 1734
 
7.7%
6 1601
 
7.1%
5 1399
 
6.2%
3 1216
 
5.4%
13 1175
 
5.2%
9 615
 
2.7%
Other values (9) 946
 
4.2%
ValueCountFrequency (%)
0 439
 
1.9%
1 5909
26.2%
2 3895
17.2%
3 1216
 
5.4%
4 2178
 
9.6%
5 1399
 
6.2%
6 1601
 
7.1%
7 1918
 
8.5%
8 1734
 
7.7%
9 615
 
2.7%
ValueCountFrequency (%)
19 26
 
0.1%
18 15
 
0.1%
17 11
 
< 0.1%
16 3
 
< 0.1%
15 3
 
< 0.1%
13 1175
5.2%
12 146
 
0.6%
11 123
 
0.5%
10 180
 
0.8%
9 615
2.7%

Interactions

2023-08-30T21:45:45.648704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.265095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.880802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.322330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.769644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.278132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.719541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.191721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.708324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.359188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.937753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.380092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.828155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.335812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.779905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.250428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.764539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.449898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.990898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.432859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.881454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.389455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.835809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.304866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.821178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.589908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.044747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.486315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.936020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.442117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.892489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.360927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.878463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.647101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.098647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.541145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.990352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.496402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.954305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.417059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:46.001945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.701550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.152366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.594133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.102231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.547597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.010463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.471599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:46.064536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.761590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.209543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.652820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.161506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.606147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.071444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.531825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:46.123026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:42.820707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.265252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:43.709015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.218968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:44.661762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.130008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-30T21:45:45.588876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-30T21:45:49.516929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EdadMonto ActualCantidad de HijosChurn ProbabilityLapsed ProbabilityCantidad Cuotas Pagadas GlobalCantidad Cuotas No Pagadas GlobalEdad_donacionGéneroEstado CivilTiene hijosOtra Clasificación RFM Actual
Edad1.0000.0580.2760.086-0.1090.3830.1120.3700.0660.2110.2820.174
Monto Actual0.0581.0000.0070.002-0.021-0.069-0.129-0.1340.0000.0170.0070.037
Cantidad de Hijos0.2760.0071.0000.0650.011-0.080-0.049-0.0940.0220.1520.4940.027
Churn Probability0.0860.0020.0651.000-0.3170.045-0.263-0.0300.1160.1050.1160.293
Lapsed Probability-0.109-0.0210.011-0.3171.000-0.2510.288-0.1350.0150.0330.0300.141
Cantidad Cuotas Pagadas Global0.383-0.069-0.0800.045-0.2511.0000.2790.9250.0800.0780.2140.338
Cantidad Cuotas No Pagadas Global0.112-0.129-0.049-0.2630.2880.2791.0000.4750.0540.0230.1270.132
Edad_donacion0.370-0.134-0.094-0.030-0.1350.9250.4751.0000.0830.0770.2570.322
Género0.0660.0000.0220.1160.0150.0800.0540.0831.0000.0910.0240.062
Estado Civil0.2110.0170.1520.1050.0330.0780.0230.0770.0911.0000.4860.079
Tiene hijos0.2820.0070.4940.1160.0300.2140.1270.2570.0240.4861.0000.112
Otra Clasificación RFM Actual0.1740.0370.0270.2930.1410.3380.1320.3220.0620.0790.1121.000

Missing values

2023-08-30T21:45:46.220524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-30T21:45:46.388333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-30T21:45:46.548574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Tipo de registro del contactoFecha de nacimientoFecha Aniversario PagoEdadFecha de CaptaciónMonto ActualGéneroEstado CivilCantidad de HijosTiene hijosOcupaciónChurn ProbabilityLapsed ProbabilityRFM Segmento ActualOtra Clasificación RFM ActualCantidad Cuotas Pagadas GlobalCantidad Cuotas No Pagadas GlobalCampaña Inicial: NombreDonante ActivoEdad_donacion
PSN
1020456818Donante1964-06-162023-07-0159.02021-06-1930000.0MasculinoCasado0.0NoNaN0.59000.000Otra ClasificaciónConstantes260COF2FR.F. 202112
1020483942Donante1975-08-282023-10-0147.02022-10-0150000.0FemeninoCasado1.0SiPolitico0.78000.000Otra ClasificaciónConstantes100COWCB R.F.202211
1020276927Donante1941-09-232023-01-3181.02015-12-2920000.0MasculinoCasado2.0SiJubilado0.53000.000Otra ClasificaciónConstantes8110SPIF F2F 201618
1020476173Donante1988-10-282023-04-1834.02022-04-1730000.0FemeninoSoltero0.0NoIngeniero0.95000.000Otra ClasificaciónConstantes160COF2FR.F.202211
1020483943Donante1982-04-222023-10-0141.02022-10-0130000.0FemeninoCasado2.0SiEmpleado0.92000.000Otra ClasificaciónConstantes100COWCB R.F.202211
1020276943Donante1985-03-042023-01-3138.02015-12-2920000.0FemeninoConcubinato2.0SiNaN0.01040.581Otra ClasificaciónConstantes3759SPIF F2F 201618
1020483945Donante1994-04-172023-12-0129.02022-10-0130000.0FemeninoCasado1.0SiAbogado0.02990.000Otra ClasificaciónDistantes63COWCB R.F.202211
1020476245Donante1988-08-092023-05-0134.02022-04-1730000.0MasculinoSoltero1.0SiDiseñador0.87000.000Otra ClasificaciónConstantes150COF2FR.F.202211
1020483951Donante1992-11-282023-10-1230.02022-10-0150000.0FemeninoCasado1.0SiEmpleado0.01280.000Otra ClasificaciónConstantes101COF2FR.F.202211
1020438938Donante1979-05-282023-01-0844.02018-12-1050000.0FemeninoCasado2.0SiIngeniero0.31000.000Otra ClasificaciónConstantes3620SOYSUVOZ_Facebook-Ads15
Tipo de registro del contactoFecha de nacimientoFecha Aniversario PagoEdadFecha de CaptaciónMonto ActualGéneroEstado CivilCantidad de HijosTiene hijosOcupaciónChurn ProbabilityLapsed ProbabilityRFM Segmento ActualOtra Clasificación RFM ActualCantidad Cuotas Pagadas GlobalCantidad Cuotas No Pagadas GlobalCampaña Inicial: NombreDonante ActivoEdad_donacion
PSN
1020436623Donante1987-01-182023-10-0136.02018-09-1430000.0FemeninoSoltero1.0SiIngeniero0.420.0Otra ClasificaciónConstantes590F2F RE-INVERSIÓN 201815
1020436631Donante1974-07-302023-12-2049.02018-09-2330000.0FemeninoSoltero1.0SiIngeniero0.390.0Otra ClasificaciónConstantes4513IF4C F2F II 201815
1020436658Donante1990-12-262023-10-3132.02018-09-1950000.0FemeninoSoltero1.0SiIngeniero0.200.0Otra ClasificaciónConstantes576COWCB R.F.202215
1020436681Donante1966-05-192023-10-2557.02018-09-1825000.0MasculinoCasado2.0SiEmpleado0.270.0Otra ClasificaciónConstantes580F2F RE-INVERSIÓN 201815
1020436695Donante1961-01-012023-01-3062.02018-09-3050000.0FemeninoCasado2.0SiAbogado0.290.0Otra ClasificaciónConstantes508F2F REINV 2018 PARA 201915
1020436727Donante1978-06-272023-10-0545.02018-09-2950000.0MasculinoCasado2.0SiIngeniero0.300.0Otra ClasificaciónIdeales571IF4C F2F II 201815
1020438489Donante1987-06-192023-10-2536.02018-10-1350000.0MasculinoConcubinato1.0SiEmpleado0.300.0Otra ClasificaciónConstantes580F2F RE-INVERSIÓN 201815
1020438498Donante1971-08-192023-01-0251.02018-10-1360000.0MasculinoCasado2.0SiDiseñador0.220.0Otra ClasificaciónConstantes560F2F REINV 2018 PARA 201915
1020438513Donante1979-06-042023-02-1544.02018-10-1515000.0FemeninoSoltero1.0SiQuímico Farmacéutico0.370.0Otra ClasificaciónConstantes486F2F REINV 2018 PARA 201915
1020438536Donante1976-12-192023-11-0146.02018-10-1335000.0MasculinoConcubinato3.0SiPolicía/Militar0.260.0Otra ClasificaciónConstantes570IF4C F2F II 201815